Polynomial Regression.ipynb

{
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"
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    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Polynomial Regression\n",
    "\n",
    "#### Polynomial regression is a special case of linear regression where we fit a polynomial equation on the data with a curvilinear relationship between the target variable and the independent variables.\n",
    "\n",
    "In Linear Regression, with a single predictor, we have the following equation:\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "where,\n",
    "\n",
    "          Y is the target,\n",
    "          x is the predictor,\n",
    "          𝜃0 is the bias,\n",
    "          and 𝜃1 is the weight in the regression equation\n",
    "\n",
    "This linear equation can be used to represent a linear relationship. But, in polynomial regression, we have a polynomial equation of degree n represented as:\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    " 𝜃0 is the bias,\n",
    "\n",
    "          𝜃1, 𝜃2, …, 𝜃n are the weights in the equation of the polynomial regression,\n",
    "          and n is the degree of the polynomial\n",
    "\n",
    "The number of higher-order terms increases with the increasing value of n, and hence the equation becomes more complicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/position_salaries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Position</th>\n",
       "      <th>Level</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Business Analyst</td>\n",
       "      <td>1</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Junior Consultant</td>\n",
       "      <td>2</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Senior Consultant</td>\n",
       "      <td>3</td>\n",
       "      <td>60000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Manager</td>\n",
       "      <td>4</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Country Manager</td>\n",
       "      <td>5</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Position  Level  Salary\n",
       "0   Business Analyst      1   45000\n",
       "1  Junior Consultant      2   50000\n",
       "2  Senior Consultant      3   60000\n",
       "3            Manager      4   80000\n",
       "4    Country Manager      5  110000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[[\"Level\"]]\n",
    "y = df[\"Salary\"]\n",
    "# Splitting the dataset into the Training set and Test set\n",
    "from sklearn.model_selection import train_test_split \n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, color='red')\n",
    "plt.title('Truth or Bluff (Linear Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X, y, color='red')\n",
    "plt.plot(X, lin_reg.predict(X), color='blue')\n",
    "plt.title('Linear Regression')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The implementation of polynomial regression is a two-step process. First, we transform our data into a polynomial using the PolynomialFeatures function from sklearn and then use linear regression to fit the parameters:\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "poly_reg = PolynomialFeatures(degree=4)\n",
    "X_poly = poly_reg.fit_transform(X)\n",
    "pol_reg = LinearRegression()\n",
    "pol_reg.fit(X_poly, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " I have taken a 4-degree polynomial. We can choose the degree of polynomial based on the relationship between target and predictor. The 1-degree polynomial is a simple linear regression; therefore, the value of degree must be greater than 1.\n",
    "\n",
    "With the increasing degree of the polynomial, the complexity of the model also increases. Therefore, the value of n must be chosen precisely. If this value is low, then the model won’t be able to fit the data properly and if high, the model will overfit the data easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Polymonial Regression results\n",
    "plt.scatter(X, y, color='red')\n",
    "plt.plot(X, pol_reg.predict(poly_reg.fit_transform(X)), color='blue')\n",
    "plt.title('Linear Regression')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([249500.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting a new result with Linear Regression\n",
    "lin_reg.predict([[5.5]])\n",
    "#output should be 249500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([132148.43750003])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting a new result with Polymonial Regression\n",
    "pol_reg.predict(poly_reg.fit_transform([[5.5]]))\n",
    "#output should be 132148.43750003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6690412331929895"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9973922891706614"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_reg.score(X_poly,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/mtcars.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>drat</th>\n",
       "      <th>wt</th>\n",
       "      <th>qsec</th>\n",
       "      <th>vs</th>\n",
       "      <th>am</th>\n",
       "      <th>gear</th>\n",
       "      <th>carb</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6</td>\n",
       "      <td>160.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>22.8</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>93</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>21.4</td>\n",
       "      <td>6</td>\n",
       "      <td>258.0</td>\n",
       "      <td>110</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>18.7</td>\n",
       "      <td>8</td>\n",
       "      <td>360.0</td>\n",
       "      <td>175</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               model   mpg  cyl   disp   hp  drat     wt   qsec  vs  am  gear  \\\n",
       "0          Mazda RX4  21.0    6  160.0  110  3.90  2.620  16.46   0   1     4   \n",
       "1      Mazda RX4 Wag  21.0    6  160.0  110  3.90  2.875  17.02   0   1     4   \n",
       "2         Datsun 710  22.8    4  108.0   93  3.85  2.320  18.61   1   1     4   \n",
       "3     Hornet 4 Drive  21.4    6  258.0  110  3.08  3.215  19.44   1   0     3   \n",
       "4  Hornet Sportabout  18.7    8  360.0  175  3.15  3.440  17.02   0   0     3   \n",
       "\n",
       "   carb  \n",
       "0     4  \n",
       "1     4  \n",
       "2     1  \n",
       "3     1  \n",
       "4     2  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop(columns=[\"model\",\"mpg\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df[\"mpg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_reg = PolynomialFeatures(degree=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_poly=poly_reg.fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   6., 160., ...,  64.,  64.,  64.],\n",
       "       [  1.,   6., 160., ...,  64.,  64.,  64.],\n",
       "       [  1.,   4., 108., ...,  16.,   4.,   1.],\n",
       "       ...,\n",
       "       [  1.,   6., 145., ..., 150., 180., 216.],\n",
       "       [  1.,   8., 301., ..., 200., 320., 512.],\n",
       "       [  1.,   4., 121., ...,  32.,  16.,   8.]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_poly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8690157644777647"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_reg.fit(x_poly,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pol_reg.score(x_poly,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline_lr=Pipeline([('transform',PolynomialFeatures(degree=1)),('model',LinearRegression())])\n",
    "model = pipeline_lr.fit(x, y)\n",
    "model.score(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"../Data/position_salaries.csv\")\n",
    "x = df[[\"Level\"]]\n",
    "y = df[\"Salary\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_reg = PolynomialFeatures(degree=1)\n",
    "x_poly=poly_reg.fit_transform(x)\n",
    "pol_reg.fit(x_poly,y)\n",
    "ypred1=pol_reg.predict(x_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_reg = PolynomialFeatures(degree=2)\n",
    "x_poly=poly_reg.fit_transform(x)\n",
    "pol_reg.fit(x_poly,y)\n",
    "ypred2=pol_reg.predict(x_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_reg = PolynomialFeatures(degree=3)\n",
    "x_poly=poly_reg.fit_transform(x)\n",
    "pol_reg.fit(x_poly,y)\n",
    "ypred3=pol_reg.predict(x_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_reg = PolynomialFeatures(degree=4)\n",
    "x_poly=poly_reg.fit_transform(x)\n",
    "pol_reg.fit(x_poly,y)\n",
    "ypred4=pol_reg.predict(x_poly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ae6bd03940>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,label=\"data points\")\n",
    "plt.plot(x,ypred1, color='r',label=\"Degree 1\")\n",
    "plt.plot(x,ypred2, color='g',label=\"Degree 2\")\n",
    "plt.plot(x,ypred3, color='m',label=\"Degree 3\")\n",
    "plt.plot(x,ypred4, color='k',label=\"Degree 4\")\n",
    "plt.legend()"
   ]
  }
 ],
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